A. Field of the Invention
The present invention relates generally to loyalty and retention programs and, more particularly, to the use of data modeling to guide such programs.
B. Description of the Prior Art
Businesses today focus efforts on both generating new customers and retaining existing customers. Typically, companies tend to look only at when a customer's contract expires to engage in retention efforts, and they apply a standard marketing strategy for all customers without taking into account the customer's previous history with the company. By using this approach for customer retention, a company often wastes time with a customer who generates little revenue for the company, while not spending enough time trying to keep a customer who is expected to generate a lot of future revenue for the company. Further, a company may apply the same incentives to both categories of customers.
Another problem with this retention technique is that contract expiration is not the only time that a company should solicit the renewal of a contract. Sometimes it is better to contact the customer after a contract has been renewed, or further, not to contact the customer at all.
Some companies compare data models with new customer information to make determinations about how to approach certain aspects of a customer's account, including how long a customer will stay with the company, also known as survival. Some statistical approaches for the analysis of survival data deal with noting observations about “hazard functions” where the parameters for each technique vary. A hazard function is a formula representing the probability of a customer's termination of service based on previous behavior derived from a stored data set. FIG. 1 graphically represents an example of a hazard function. Hazard values, which represent the probability of a customer churning, or terminating the contract, are plotted along the y-axis. The age of the customer is plotted along the x-axis. The graph displays a spike at 12 months which demonstrates that this customer is more likely to cease business dealings with the company at 12 months, which coincides with the termination of that customer's contract.
Parametric survival models estimate the effects of covariates (subject variables whose values influence independent variables) by presuming a lifetime distribution of a known form, such as an exponential or Weibull. Although such models are popular for some applications, including accelerated failure models, the smoothness of these postulated distributions makes them inappropriate for survival data with contract expiration dates that provide natural spikes in the models for such data.
In contrast, the Kaplan-Meier method is “non-parametric” and provides hazard and survival functions with no assumption of a parametric lifetime distribution function. However, it is difficult to use this method to estimate the effects of covariates on the hazard and survival functions. Subsets of customers can generate separate Kaplan-Meier estimates, but sample size considerations generally require substantial aggregation in the data so that many customers are assigned the same hazard and survival functions regardless of their variation on many potential covariates.
The proportional hazard method assumes that each customer's hazard function is a multiple of a single baseline hazard. This multiplier uses functions that are generally linear which tends to assign extreme values to subjects with extreme covariate values. Further, the presumption of the proportional hazards is restrictive in that there may not be a single baseline hazard for each subject, and the form of that baseline's variation may not be well modeled by the time-dependent covariates or stratification that are the traditional statistical extensions of the original proportional hazard model.
Companies also use neural networks for predictive modeling and modeling relationships whose form is unknown. Because neural networks are non-linear, universal function approximators, they overcome the proportionality and linearity constraints imposed by the statistical approaches for modeling survival data. Neural networks have previously been used to predict actual tenure of a customer, but the information generated by these neural networks falls short in utilizing this information to focus marketing techniques as the information only speaks to the actual tenure of the customer, not the probable future tenure of the customer.
In addition to the models stated above, companies use the concept of customer lifetime value in order to value customers. Customer lifetime value (LTV) is the measure of the profit generating potential, or value, of a customer and is a useful tool in evaluating the high-value customers. LTV is composed of two independent components, value and tenure. Value incorporates the concepts of account revenue and fixed and variable costs. It is important in the prediction of LTV to incorporate the estimated differentiated tenures for every customer with a given service supplier, based on the usage, revenue, and sales profiles contained in company databases. Tenure prediction models generate, for a given customer, i, a hazard curve or a hazard function that indicates the probability hi(t) of cancellation at a given time, t, in the future. A hazard curve can be converted to a survival curve or a survival function, which plots the probability Si(t) of survival, or non-cancellation, at any time, t, given that customer, i, was active at time (t−e1), i.e., Si(t)=Si(t−1)×[1−hi(t)] with Si(1)=1. As such, the LTV=Σ(t=1,T)Si(t)×vi(t) where vi(t) is the expected value of customer, i, at time, t, and T is the maximum time period under consideration. This LTV calculates customer specific estimates of total expected future profit based on customer behavior and usage patterns.
One of the shortcomings of using valuation by revenue and LTV is that customer valuations by revenue and LTV ignore the potential effects of company actions, particularly retention and service actions.
It is therefore desirable to provide techniques that overcome the limitations of existing methods to calculate probabilities of tenure of a customer to focus marketing techniques.